The introduction of the SELD task in the 2019 Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) has significantly accelerated SELD research. Many significant contributions have been made over the last three years in terms of datasets, evaluation metrics, and algorithms [1]}. The TAU Spatial Sound Events dataset [2]} used in DCASE 2019 included only stationary sound sources, with 72 room impulse responses (RIRs) from 5 different locations, and only 5 distinct samples for each of the 11 sound classes. The TAU-NIGENS Spatial Sound Events dataset [3]} used in DCASE 2020 saw an introduction of moving sound sources, more RIRs from 15 different locations, and 14 sound classes extracted from the NIGENS General Sound Events Database [4]}, with around 30 to 50 distinct samples per class. The 2021 edition [5]} introduced unknown directional interferences, making the sound scenes more realistic, in addition to the increase in the maximum polyphony of target events to three, from two in the 2019 and 2020 runs. The number of sound classes was reduced to 12, as some classes were used as interferences.
All three SELD datasets provide both first-order ambisonic (FOA) and microphone array (MIC) formats.
